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CPS1999 Pranesh K. et al.
squares model, upper limits or lower limits of prediction interval are computed
by taking the coefficients as their corresponding estimated value plus or minus
standard error, i.e. using the equation,

= (23.5114 ± 0.8360) + (−0.0614 ± 0.0370). (7)

Similarly, the limits of fuzzy regression model are computed by the equation

= (23.5713 ± 0.4950) + (−0.0729 ± 0.0325). (8)

The width of predicted intervals of fuzzy regression model is much smaller
than that of ordinary least squares model, which indicates the superiority of
fuzzy regression methodology.

2
Figure 1: Ocean Heat content (in million km ) and Global Sea Ice Extent (in
2
million km ).

3.3. Error Analysis
To make performance comparisons, we have calculated prediction errors
denoted by = − and = − and

corresponding standardized errors = , and =

, where and denote the standard errors. Further, root mean

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